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Study Area and Design We conducted our study in coastal wetlands throughout the entire Great Lakes basin (see Figure 1 in Tozer et al.). We selected coastal wetlands using a stratified, random sampling protocol (Uzarski et al. 2017, 2019). Further details regarding the study design are in Burton et al. (2008). The sampling universe was all coastal wetlands greater than 4 ha in size with a permanent or periodic surface-water connection to an adjacent Great Lake or their connecting river systems (Uzarski et al. 2017). We stratified our selection of wetlands for the study by 1) wetland hydrogeomorphic type (riverine, lacustrine, barrier protected; Albert et al. 2005), 2) region (northern or southern; Danz et al. 2005), and 3) lake (i.e., the watershed of 1 of the 5 Great Lakes). We sampled approximately 20% of all wetlands in each stratum each year, so that nearly all coastal wetlands within the Great Lakes basin meeting the selection criteria were sampled at least once every 5 years. In addition, we resampled 10% of wetlands between years according to a rotating panel design. Sampled wetlands were dominated by emergent, herbaceous vegetation and shallow water ( 250 m apart to avoid double counting individuals. We surveyed each point count location twice per year, at least 15 days apart, between 20 May and 10 July, which was the peak breeding period for marsh birds in the study area. Surveys took place either in the morning (30 min before sunrise to 4 h after sunrise) or the evening (4 h before sunset to 30 min after sunset), with 1 or both of the 2 surveys being in the morning each year (Tozer et al. 2017). We conducted surveys only when there was no precipitation and wind was < 20 km/h (Beaufort 3 or less). Each point count survey lasted 10 min, consisting of an initial 5-min passive listening period followed by a 5-min call broadcast period. The call broadcast period was intended to increase detections of secretive species by eliciting auditory responses and was composed of 30 sec of vocalizations followed by 30 sec of silence for each of the following: 1) Least Bittern, 2) Sora, 3) Virginia Rail, 4) a mixture of American Coot and Common Gallinule, and 5) Pied-billed Grebe, in that order. We trained observers so they thoroughly understood the field protocols and we required each observer to pass an aural and visual bird identification test in order to collect data. CWMP bird surveys were 15 min in duration from 2011 to 2018 but were reduced to 10 min from 2019 to 2021 (Tozer et al. 2017). To accommodate changes in survey protocol, we filtered the data to only include birds detected in the first 10 min of point counts from 2011 to 2018. For a detailed description of the sampling protocol visit greatlakeswetlands.org/Sampling-protocols. Response Variable The response variable for each species was the maximum number of individuals observed during either of the 2 surveys at each point count location in each year (Tozer 2020, Hohman et al. 2021). We viewed these counts as indices of true density, meaning our modeled values estimated relative abundance (e.g., Thogmartin et al. 2004). We assumed that variation in species-specific detection was uncorrelated with the predictors in our models, including year. This was sufficient in our case because our objective was to quantify relative differences and changes in abundance and not to quantify actual density. Our assumption was warranted because our data were collected using standardized methods designed to reduce heterogeneity in detection, e.g., observer training and testing, as well as restrictions on survey date, time of day, and wind (Conway 2011, Uzarski et al. 2017). It was further justified by other long-term, broad-scale studies of birds based on point counts conducted using similar standardized approaches, which found no differences in year or covariate effects based on counts that were adjusted or unadjusted for detection (Etterson et al. 2009, Zlonis et al. 2019). We note that long-term (1996–2013) marsh-breeding bird monitoring data collected throughout the developed, southern portion of the Great Lakes basin showed no systematic trends in detectability over time for 14 of 15 (93%) species (Tozer 2016). We also found no trends in detectability across years for all of the species in our dataset (see Supplemental Material Figure S1 in Tozer et al.), meaning that differences in detection did not bias our estimates of annual abundance indices or trends. Therefore, we did not adjust for detectability, which has been supported, for instance, by Hutto (2016) and Johnson (2008). The dataset consisted of 8,120 surveys completed at 1,962 point count locations in 792 coastal wetlands in 599 watersheds (defined by Forsyth et al. [2016]) over 11 years (2011–2021; see Figure 1, 2 and Supplemental Material Table S1 in Tozer et al.). There were 2.2 ± 1.6 (mean ± SD) point count locations per wetland (range: 1–8) and 1.3 ± 0.9 wetlands per watershed (range: 1–9). In total, we analyzed 18 species: 1) American Bittern, 2) American Coot, 3) Black Tern, 4) Common Gallinule, 5) Common Grackle, 6) Common Yellowthroat, 7) Forster's Tern, 8) Least Bittern, 9) Marsh Wren, 10) Mute Swan, 11) Pied-billed Grebe, 12) Red-winged Blackbird, 13) Sandhill Crane, 14) Sedge Wren, 15) Sora, 16) Swamp Sparrow, 17) Virginia Rail, and 18) Wilson's Snipe. We chose these species because they were of conservation interest in the Great Lakes region (e.g., Bianchini and Tozer 2023) and regularly nested or foraged in Great Lakes coastal wetlands. We attempted to model abundance and trends for Trumpeter Swan (Cygnus buccinator) and Yellow-headed Blackbird (Xanthocephalus xanthocephalus), but data were too sparse for the models to converge. We considered some regions of our study area to be out of range for some species. We accounted for this by dividing our study area into 10 regions and dropped any of them from species-specific analyses if naive occupancy was < 5% (Supplemental Material Table S2). By excluding out-of-range point count locations, we reduced the number of zero counts and focused our analysis on point count locations where zero counts were most likely to represent legitimate absences. As a result, the number of marsh-breeding bird species for which we quantified abundance and trends varied by lake due to uneven species occurrences across the study area: Superior (n = 10), Ontario (n = 12), Erie (n = 16), Huron (n = 16), and Michigan (n = 17). The CWMP bird survey data are available by request at greatlakeswetlands.org. Environmental Predictors We included the following environmental predictors in our models, which were known to influence abundance of marsh-breeding birds in the Great Lakes: 1) percent local wetland cover within 250 m of point count locations (as a proxy for wetland size; e.g., Studholme et al. 2023), 2) detrended, standardized Great Lakes water levels (to avoid correlation with year; e.g., Hohman et al. 2021, Denomme-Brown et al. 2023), 3) percent urban land cover in the surrounding watershed (e.g., Rahlin et al. 2022), and 4) percent agricultural land cover in the surrounding watershed (e.g., Saunders et al. 2019). The land cover predictors were static covariates (i.e., they were the same for all years), whereas detrended, standardized Great Lakes water level was a dynamic covariate (i.e., it varied annually). Land cover and water-level information at finer spatial and temporal scales would have been preferred, but such data were unavailable. Nonetheless, it is reasonable to assume that the land cover and water-level data we used provided useful approximations of the true values, particularly at the watershed scale (e.g., Michaud et al. 2022). Percent local wetland cover was based on the coastal wetland layer built by the Great Lakes Coastal Wetland Consortium (Burton et al. 2008, Uzarski et al. 2017), and percent urban and agricultural land cover were from Host et al. (2019) with watersheds defined by Forsyth et al. (2016); all of these data are available at glahf.org/data. We used ArcGIS 10.8.1 to overlay CWMP sample points onto the land cover layers and extracted the relevant predictors for each point (see Figure 3 in Tozer et al.). Yearly water levels were from the National Oceanic and Atmospheric Administration (noaa.gov). We used the mean yearly water level from May to July since these months overlapped with our survey period. We detrended water levels from year by using the residuals from a line of best fit for each lake, given that water levels generally increased in all lakes over the course of the study. Water levels were also standardized across lakes by dividing the annual value for each lake by the long-term mean (2011–2021) for each lake, given the reference value is the same for all lakes (International Great Lakes Datum 1985). Our detrended, standardized lake levels therefore represent water levels without being confounded with year (see Figure 4 in Tozer et al.). The environmental predictors were not correlated (-0.2 < r < 0.2; see Supplemental Material Figure S2 in Tozer et al.). Statistical Modeling We fit models in a Bayesian framework with Integrated Nested Laplace Approximation (INLA) using the R-INLA package (Rue and Martino 2009) for R statistical computing (version 4.2.0; R Core Team 2022). For each species, we modeled the expected (predicted mean) number of individuals per point count location in each Great Lake in each year, as well as the trend in these values across years in each lake, and then pooled the lake-specific trends to obtain Great Lakes-wide estimates. We included spatial structure in the models using an intrinsic conditional autoregressive (iCAR) structure (Besag et al. 1991), which allowed for information on relative abundance to be shared across lakes sharing basin boundaries. By accounting for this spatial structure in counts, the model allowed abundance and trend information to be shared among adjacent lakes (as described below), which improved estimates for lakes with limited sample sizes (Bled et al. 2013) and reduced the amount of spatial autocorrelation in model residuals (Zuur et al. 2017). We modeled counts уi,j,t using the maximum number of individuals observed at a point count location within a given wetland (j), lake (i), and year (t). The expected counts per lake within a given year µi,t for each of the 18 species took the form: log(µit) = αi + τiΤi,j,t + κj + ρj + уi,t + β1Wj + β2Lj + β3Uj + β4Ai where α = random lake intercept; T = year, indexed to 2021; τ = random lake slope effect; κ = random wetland effect; ρ = random wetland type effect; and у = random lake-year effect. Environmental predictors included: W = percent local wetland cover within 250 m; L = detrended, standardized water level; U = percent urban land cover in the surrounding watershed; and A = percent agricultural land cover in the surrounding watershed. The random lake intercept (αi) had an iCAR structure, where values of αi came from a normal distribution with a mean value related to the average of adjacent lakes. The random lake intercept also had a conditional variance proportional to the variance across adjacent lakes and inversely proportional to the number of adjacent lakes. We modeled the random lake slopes (τi) as spatially structured, lake-specific, random slope coefficients for the year effect, using the iCAR structure, with conditional means and variances as described above. We incorporated spatial structure into the random lake slopes (τi) to allow for information about year effects to be shared across neighboring lakes, and to allow year effects to vary among lakes. We transformed year (T) such that the maximum year was 0, and each preceding year was a negative integer. This scaling meant that the estimates of the random lake intercepts (αi) could be interpreted as the lake-specific expected counts (i.e., index of abundance) during the final year of the time series. We accounted for differences in relative abundance among wetlands (κ) and wetland types (ρ) with an independent and identically distributed (idd) random effect. To derive an annual index of abundance per lake, we included a random effect per lake-year (у) with an idd, and combined these effects with α and τ. Β1, β2, β3, and β4 were given normal priors with mean of zero and precision equal to 0.001. We scaled the spatial structure parameters α and τ such that the geometric mean of marginal variances was equal to one (Sørbye and Rue 2014, Riebler et al. 2016, Freni-Sterrantino et al. 2018), and priors for precision parameters were penalized complexity (PC) priors, with parameter values UPC = 1 and PC = 0.01 (Simpson et al. 2017). We also assigned precision for the random wetland, wetland type, and lake-year effects with a PC prior with parameter values previously stated. In general, the weakly informed priors used here tend to shrink the structured and unstructured random effects towards zero in the absence of a strong signal (Simpson et al. 2017). We validated distributional assumptions with simulation to ensure models could handle the large number of zero counts for some species. The abundance of most species was modeled using a zero-inflated Poisson (ZIP) distribution. Common Grackle and Red-winged Blackbird, which were more frequently detected compared to the other species, better fit a negative binomial distribution, and Common Yellowthroat better fit a Poisson distribution. We further validated models by visually inspecting 1) the fit versus raw counts; 2) residuals versus predictors; and 3) the estimate for Ф, the dispersion parameter (Zuur and Ieno 2016). Our visual inspections of fit versus raw counts suggested models were not overfit and were able to capture the variation of the raw counts. In general, residuals versus fit values behaved randomly around the zero line and residuals appeared to behave randomly with each predictor, suggesting the models fit well. The dispersion statistics were around 1 for all species, ranging lowest for Common Yellowthroat (0.72) and highest for Mute Swan (3.38), suggesting some residual under and over dispersion, respectively. Mute Swan had some high counts (outliers) which may have contributed to this. Following model analysis, we computed posterior estimates of trends (τ) and associated credible intervals for the full extent of the study area (i.e., by pooling lake-specific trends) using lake watershed size to calculate area-weighted averages (Link and Sauer 2002). References Albert, D. A., D. A. Wilcox, J. W. Ingram, and T. A. Thompson (2005). Hydrogeomorphic classification for Great Lakes coastal wetlands. Journal of Great Lakes Research 31:129–146. Besag, J., J. York, and A. Mollié (1991). Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics 43:1–20. Bianchini, K., and D. C. Tozer (2023). Using Breeding Bird Survey and eBird data to improve marsh bird monitoring abundance indices and trends. Avian Conservation and Ecology 18(1):4. Bled, F., J. Sauer, K. Pardieck, P. Doherty, and J. A. Royle (2013). Modeling trends from North American breeding bird survey data: a spatially explicit approach. PLoS ONE 8, e81867. Burton, T. M., J. C. Brazner, J. J. H. Ciborowski, G. P. Grabas, J. Hummer, J. Schneider, and D. G. Uzarski (Editors) (2008). Great Lakes Coastal Wetlands Monitoring Plan. Developed by the Great Lakes Coastal Wetlands Consortium, for the US EPA, Great Lakes National Program Office, Chicago, IL. Great Lakes Commission, Ann Arbor, Michigan, USA. Conway, C. J. (2011). Standardized North American marsh bird monitoring protocol. Waterbirds 34:319–346. Danz, N. P., R. R. Regal, G. J. Niemi, V. J. Brady, T. Hollenhorst, L. B. Johnson, G. E. Host, J. M. Hanowski, C. A. Johnston, T. Brown, J. Kingston, and J. R. Kelly (2005). Environmentally stratified sampling design for the development of Great Lakes environmental indicators. Environmental Monitoring and Assessment 102:41–65. Denomme-Brown, S. T., G. E. Fiorino, T. M. Gehring, G. J. Lawrence, D. C. Tozer, and G. P. Grabas (2023). Marsh birds as ecological performance indicators for Lake Ontario outflow regulation. Journal of Great Lakes Research 49:479–490. Etterson, M. A., G. J. Niemi, and N. P. Danz (2009). Estimating the effects of detection heterogeneity and overdispersion on trends estimated from avian point counts. Ecological Applications 19:2049–2066. Forsyth, D. K., C. M. Riseng, K. E. Wehrly, L. A. Mason, J. Gaiot, T. Hollenhorst, C. M. Johnston, C. Wyrzykowski, G. Annis, C. Castiglione, K. Todd, et al. (2016) The Great Lakes hydrography dataset: consistent, binational watersheds for the Laurentian Great Lakes basin. Journal of the American Water Resources Association 52:1068–1088. Freni-Sterrantino, A., M. Ventrucci, and H. Rue (2018). A note on intrinsic conditional autoregressive models for disconnected graphs. Spatial and Spatio-temporal Epidemiology 26:25–34. Hohman, T. R., R. W. Howe, D. C. Tozer, E. E. Gnass Giese, A. T. Wolf, G. J. Niemi, T. M. Gehring, G. P. Grabas, and C. J. Norment (2021). Influence of lake-levels on water extent, interspersion, and marsh birds in Great Lakes coastal wetlands. Journal of Great Lakes Research 47:534–545. Host, G. E., K. E. Kovalenko, T. N. Brown, J. J. H. Ciborowski, and L. B. Johnson (2019). Risk-based classification and interactive map of watersheds contributing anthropogenic stress to Laurentian Great Lakes coastal ecosystems. Journal of Great Lakes Research 45:609–618. Hutto, R. L. (2016). Should scientists be required to use a model-based solution to adjust for possible distance-based detectability bias? Ecological Applications 26:1287–1294. Johnson, D. H. (2008). In defense of indices: the case of bird surveys. Journal of Wildlife Management 72:857–868. Link, W. A., and J. R. Sauer (2002). A hierarchical analysis of population change with application to Cerulean Warblers. Ecology 83:2832–2840. Michaud, W., J. Telech, M. Green, B. Daneshfar, and M. Pawlowski (2022). Sub-indicator: land cover. In State of the Great Lakes 2022 Technical Report. Published by Environment and Climate Change Canada and U.S. Environmental Protection Agency. R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Rahlin, A. A., S. P. Saunders, and S. Beilke (2022). Spatial drivers of wetland bird occupancy within an urbanized matrix in the upper midwestern United States. Ecosphere 13, e4232. Riebler, A., S. H. Sørbye, D. Simpson, and H. Rue (2016). An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Statistical Methods in Medical Research 25:1145–1165. Rue, H., S. Martino, and N. Chopin (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society Series B (Statistical Methodology) 71:319–392. Saunders, S. P., K. A. L. Hall, N. Hill, and N. L. Michel (2019). Multiscale effects of wetland availability and matrix composition on wetland breeding birds in Minnesota, USA. Condor 121:duz024. Simpson, D., H. Rue, A. Riebler, T. G. Martins, and S. H. Sørbye (2017). Penalising model component complexity: a principled, practical approach to constructing priors. Statistical Science 32:1–28. Sørbye, S. H., and H. Rue (2014). Scaling intrinsic Gaussian Markov random field priors in spatial modeling. Spatial Statistics 8:39–51. Studholme, K. R., G. E. Fiorino, G. P. Grabas, and D. C. Tozer (2023). Influence of surrounding land cover on marsh-breeding birds: implications for wetland restoration and conservation planning. Journal of Great Lakes Research 49:318–331. Thogmartin, W. E., J. R. Sauer JR, and M. G. Knutson (2004). A hierarchical spatial model of avian abundance with application to Cerulean Warblers. Ecological Applications 14:1766–1779. Tozer, D. C. (2016). Marsh bird occupancy dynamics, trends, and conservation in the southern Great Lakes basin: 1996 to 2013. Journal of Great Lakes Research 42:136–145. Tozer, D. C. (2020). Great Lakes Marsh Monitoring Program: 25 years of conserving birds and frogs. Birds Canada, Port Rowan, Ontario, Canada. Tozer, D. C., C. M. Falconer, A. M. Bracey, E. E. Gnass Giese, G. J. Niemi, R. W. Howe, T. M. Gerhing, and C. J. Norment (2017). Influence of call broadcast timing within point counts and survey duration on detection probability of marsh breeding birds. Avian Conservation and Ecology 12(2):8. [Tozer et al.] Tozer DC, Bracey AM, Fiorino GE, Gehring TM, Gnass Giese EE, Grabas GP, Howe RW, Lawrence GJ, Niemi GJ, Wheelock BA, Ethier DM. Increasing marsh bird abundance in coastal wetlands of the Great Lakes, 2011–2021, likely caused by increasing water levels. Ornithological Applications. Uzarski, D. G., D. A. Wilcox, V. J. Brady, M. J. Cooper, D. A. Albert, J. J. H. Ciborowski, N. P. Danz, A. Garwood, J. P. Gathman, T. M. Gehring, G. P. Grabas, et al. (2019). Leveraging a landscape-level monitoring and assessment program for developing resilient shorelines throughout the Laurentian Great Lakes. Wetlands 39:1357–1366. Uzarski, D. G., V. J. Brady, M. J. Cooper, D. A. Wilcox, D. A. Albert, R. P. Axler, P. Bostwick, T. N. Brown, J. J. H. Ciborowski, N. P. Danz, J. P. Gathman, et al. (2017). Standardized measures of coastal wetland condition: implementation at a Laurentian Great Lakes basin-wide scale. Wetlands 37:15–32. Zlonis, E. J., N. G. Walton, B. R. Sturtevant, P. T. Wolter, and G. J. Niemi (2019). Burn severity and heterogeneity mediate avian response to wildfire in a hemiboreal forest. Forest Ecology and Management 439:70–80. Zuur, A. F., and E. I. Ieno (2016). A protocol for conducting and presenting results of regression-type analyses. Methods in Ecology and Evolution 7:636–645. Zuur, A. F., E. I. Ieno, and A. A. Saveliev (2017). Beginner's guide to spatial, temporal and spatial-temporal ecological data analysis with R-INLA. Volume I: Using GLM and GLMM. Highland Statistics, Newburgh, United Kingdom. Wetlands of the Laurentian Great Lakes of North America, i.e., lakes Superior, Michigan, Huron, Erie, and Ontario, provide critical habitat for marsh birds. We used 11 years (2011–2021) of data collected by the Great Lakes Coastal Wetland Monitoring Program at 1,962 point count locations in 792 wetlands to quantify the first-ever annual abundance indices and trends of 18 marsh-breeding bird species in coastal wetlands throughout the entire Great Lakes. Nine species (50%) increased by 8–37% per year across all of the Great Lakes combined, whereas none decreased. Twelve species (67%) increased by 5–50% per year in at least 1 of the 5 Great Lakes, whereas only 3 species (17%) decreased by 2–10% per year in at least 1 of the lakes. There were more positive trends among lakes and species (n = 34, 48%) than negative trends (n = 5, 7%). These large increases are welcomed because most of the species are of conservation concern in the Great Lakes. Trends were likely caused by long-term, cyclical fluctuations in Great Lakes water levels. Lake levels increased over most of the study, which inundated vegetation and increased open water-vegetation interspersion and open water extent, all of which are known to positively influence abundance of most of the increasing species and negatively influence abundance of all of the decreasing species. Coastal wetlands may be more important for marsh birds than once thought if they provide high-lake-level-induced population pulses for species of conservation concern. Coastal wetland protection and restoration are of utmost importance to safeguard this process. Future climate projections show increases in lake levels over the coming decades, which will cause "coastal squeeze" of many wetlands if they are unable to migrate landward fast enough to keep pace. If this happens, less habitat will be available to support periodic pulses in marsh bird abundance, which appear to be important for regional population dynamics. Actions that allow landward migration of coastal wetlands during increasing water levels by removing or preventing barriers to movement, such as shoreline hardening, will be useful for maintaining marsh bird breeding habitat in the Great Lakes. Funding provided by: Long Point Waterfowl and Wetlands Research Program of Birds Canada*Crossref Funder Registry ID: Award Number: Funding provided by: Environment and Climate Change CanadaCrossref Funder Registry ID: https://ror.org/026ny0e17Award Number: 3000747437 Funding provided by: Wildlife Habitat Canada (Canada)Crossref Funder Registry ID: https://ror.org/0156t7498Award Number: 23-300 Funding provided by: Great Lakes Restoration Initiative as provided by the Great Lakes National Program Office of the United States Environmental Protection Agency**Crossref Funder Registry ID: Award Number: GL-00E00612-0 Funding provided by: Great Lakes Restoration Initiative as provided by the Great Lakes National Program Office of the United States Environmental Protection Agency*Crossref Funder Registry ID: Award Number: 00E01567 Funding provided by: Great Lakes Restoration Initiative as provided by the Great Lakes National Program Office of the United States Environmental Protection Agency*Crossref Funder Registry ID: Award Number: 00E02956

MASSIMO is a distance-independent individual-tree simulator that represents demographic processes (regeneration, growth and mortality) with empirical models that have been parameterized with data from the Swiss NFI. Tree regeneration, growth and mortality are simulated on the regular grid of sample plots of the Swiss NFI, which allows for statistically representative simulations of forest development. 

Analysis and calibration of optimal power balance rotor-effective wind speed estimation schemes for large-scale wind turbines – Code and Data This repository contains the MATLAB scripts and Simulink models associated with the paper: Pamososuryo, A. K., Spagnolo, F., Mulders, S. P. Analysis and calibration of optimal power balance rotor-effective wind speed estimation schemes for large-scale wind turbines Wind Energy Science, 2024 – Preprint available here DOI: 10.5194/wes-2024-158 Repository Purpose This archive reproduces all computational results, figures, and numerical validations presented in the manuscript. The code provides a fully operational framework for simulating, analyzing, and comparing rotor-effective wind speed (REWS) estimators based on the power balance principle. Contents PowerBalanceLOFI/ Contains low-fidelity studies, estimator calibration, and robustness analysis: Script1_WindTurbineDataCurveFitting.m: Curve fitting for turbine property scaling (inertia, rated power). Script2_FilteredDerivativeNoiseStudy.m: Evaluates numerical derivative sensitivity under noisy conditions. Script3_LuenbergerAeroPowerEstimator.m: Implements a state-estimation-based aerodynamic power estimator. Script4_EstimatorSolverComparison.m: Compares solver strategies for the REWS solver component. Script5_ContinuousSolverStability.m: Investigates stability of continuous solvers under sampling effects. .slx models: Matching Simulink files for each scenario above. PowerBalanceOpenFAST/ Includes the high-fidelity validation setup using OpenFAST: Script1_main_OpenFAST.m: Runs OpenFAST simulations to generate rotor dynamics data. Script2_OpenLoopEstimation.m: Executes the power balance wind speed estimator (PB-WSE) using measured signals. Script3_Plotting.m: Produces time series and histogram figures for REWS estimation analysis. OpenFAST.slx, OpenLoopEstimation.slx: Simulink models implementing the PB-WSE structure. dependencies/ The dependencies/ folder contains third-party functions used for plotting and figure export: export_fig/: External tool for exporting figures with high quality and transparency. Source: https://github.com/altmany/export_fig linspecer/: Color palettes for line plots with distinguishable colors. Source: MathWorks File Exchange matplotlib/: MATLAB-based colormaps mimicking Python’s matplotlib perceptually uniform colormaps. Source: MathWorks File Exchange setfigpaper/: Utility to standardize figure layout and export style. Source: https://github.com/jmrplens/SetFigPaper Estimation Architecture The proposed estimator is split into two calibrated modules: Aerodynamic Power Estimator Based on either: * Numerical derivative of rotor speed * Luenberger observer for aerodynamic torque estimation Wind Speed Estimate Solver Implemented as: * Continuous-time solver * Iterative single-step solver The final configuration—state-estimation-based aerodynamic power estimator + iterative solver—is shown to be optimal. Requirements MATLAB R2024b or newer Simulink Curve Fitting Toolbox OpenFAST 3.5.3 MATLAB/Simulink interface Citation Please cite the following work when using this repository: A. K. Pamososuryo, F. Spagnolo, S. P. Mulders Analysis and calibration of optimal power balance rotor-effective wind speed estimation schemes for large-scale wind turbines Wind Energy Science, 2024 DOI: 10.5194/wes-2024-158 Authors & Affiliations Atindriyo K. Pamososuryo, Delft Center for Systems and Control, TU Delft Fabio Spagnolo, Vestas Wind Systems A/S Sebastiaan P. Mulders, Delft Center for Systems and Control, TU Delft Contact Corresponding author: A.K.Pamososuryo@tudelft.nl

Runoff and water temperature data We estimated mean annual precipitation, averaged across each drainage area, using Google Climate Engine, March 2011 - February 2021. Where multiple temperature loggers were present in a study stream, we selected a single location based on the completeness of data in the study season and proximity to the PIT antenna. Hourly temperature measurements were converted into mean daily values. Analysis For data analysis and modeling, we excluded streams that had less than 3 years of biological data, leaving 47 stream-years. We conducted all analyses in R (version 4.0.4, R Core Team, 2018). We tested outmigration timing data for normal distribution among streams, years, and stream-years, using the shapiro.test function of the broom package. The Shapiro-Wilk test showed that all distributions were unlikely to be normally distributed (i.e. among years, p = 5.5 × 10-9 – 7.6 × 10-39 and W = 0.88 – 0.98). However, the Shapiro-Wilk test can provide small p-values for large samples and consequently provide a false negative, regarding normal distribution (among years, sample size range was 485 – 3453). Therefore, we could not rule out the possibility of assumptions being met to perform ANOVA (Analysis of Variance) tests. We did so, using the aov function of the AICcmodavg package: i) one-way, by stream, ii) a one-way, by year, iii) a two-way, by stream and year, and iv) a two-way with stream-year interaction. To isolate the effects of stream and year on variance, we performed the ANOVA tests on the maximum subset of data for which each stream had the same years of outmigration (four streams, each with the same six years of data, totaling 24 stream-years). The aictab function of the AICcmodavg package demonstrated that the two-way model with stream-year interaction was the highest performing (lowest AICc value), followed by: the two-way model, one-way by year model, and one-way by stream model. In both ANOVA tests, the year, stream, and year-stream interaction terms each had "Pr(>F)" values < 2 × 10-16. The "2-way ANOVA with interaction" (year F-value 646.58, stream F-value 349.85, year-stream interaction F-value 29.31, residuals 4.11 × 10-16) had higher F values and lower residuals than the 2-way ANOVA (year F-value 629.3, stream F-value 340.5, residuals 4.22 × 10-16). We used the TukeyHSD function of the AICcmodavg package to conduct pairwise tests for significant differences in outmigration timing distributions. Among streams, five of six pairwise differences were highly significant (p < 0.0001). Among years, all 15 pairwise comparisons were highly significant (p < 0.001). Among stream-years, 216 of 277 pair-wise comparisons were significant (p < 0.05). We checked for homoscedasticity in the interaction model, using the leveneTest function of the car library, and we found evidence that the variance across groups is significantly different. Consequently, we cannot assume homogeneity of variances in the different groups, which is typically a required assumption for conducting ANOVA tests. Since the normal distribution assumption of the one-way ANOVA was not met, we applied the Kruskal-Wallis test, as a non-parametric alternative to test for variance among streams and years, using the package rstatix. As with the ANOVA tests, we performed Kruskal-Wallis tests on the maximum subset of data for which each stream had the same years of outmigration (24 stream-years), using the functions kruskal_test, kruskal_effsize, dunn_test, and wilcox_test. Among streams, we found significant variance (p = 2.16 × 10-143), with a "small" effect size (eta-squared measure = 0.04) (Tomczak and Tomczak 2014), and 5 of 6 pairwise differences were highly significant (Dunn's test & Wilcoxon's test: p < 0.0001). Among years, we found significant variance (p = 0), with a "large" effect size (eta-squared measure = 0.17) (Tomczak and Tomczak 2014), and 13 of 15 pairwise differences were highly significant (Dunn's test & Wilcoxon's test: p < 0.0001). Modeling the effects of streamflow and water temperature on outmigration timing Modeling was limited to the 42 stream-years for which water temperature and outmigration timing data were collected. For the outmigration start date model, the runoff date range was March-April and the degree-days date range was March-April. For the outmigration end date and duration models, the runoff date range was March-June and the degree-days date range was March-April. Coefficient units are "days per daily runoff (mm)" and "days per 100 degree-days". In identifying top model(s), we did not consider degree-days to influence outmigration duration because: i) the AIC value of the runoff-only model was 1.99 less than the additive model, ii) the degree-days in the additive model had a p-value > 0.05, and iii) Mar-Jun runoff had similar coefficient effect sizes in the additive model and run-off only model (Appendix S1: Table S3). We calculated conditional coefficients (including stream, as a random effect) and marginal coefficients (excluding stream, as a random effect) of determination (R2) (Nakagawa and Schielzeth 2013), using the r.squaredGLMM function of the MuMIn package (Barton` 2020). We also reported the model coefficients and 95% confidence intervals, as measures of effect size, and generated partial dependence plots for using the plot_model function of the sjPlot package (Lüdecke 2021). Literature cited Barton`, K. (2020). MuMIn: Multi-Model Inference. R package version 1.43.17. Lüdecke, D. (2021). sjPlot: Data Visualization for Statistics in Social Science. R package version 2.8.9. Nakagawa, S., and H. Schielzeth. 2013. A general and simple method for obtaining R 2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4:133–142. Tomczak, M., and E. Tomczak. 2014. The need to report effect size estimates revisited. An overview of some recommended measures of effect size 1:7. Prolonged migration windows buffer migratory animal populations against uncertainty in resource availability. Understanding how intensifying droughts from climate change influence the migration window is critical for biodiversity conservation in a warming world. We explored how drought affects the seaward migration of endangered coho salmon (Oncorhynchus kisutch) near the southern extent of their range in California, USA. We tracked stream departures of juvenile coho, measuring streamflow and temperature in 7 streams over 13 years, spanning an historic drought with extreme dry and warm conditions. Linear mixed effects models indicate that, over the range of observations, a decrease in seasonal streamflow (from 4.5 to 0.5 mm/day seasonal runoff) contracted the migration window by 31% (from 11 to 7 weeks). An increase from 10.2 to 12.8 ℃ in mean seasonal water temperature hastened the migration window by three weeks. Pacific salmon have evolved to synchronize ocean arrival with productive ocean upwelling. However, earlier and shorter migration windows during drought could lead to mismatches, decreasing fitness and population stability. Our study demonstrates that drought-induced low flows and warming threaten coho salmon in California and suggests that environmental flow protections will be needed to support the seaward migration of Pacific salmon in a changing climate. Please see DataS1/data/README_Metadata.pdf.Funding provided by: California Department of Fish and WildlifeCrossref Funder Registry ID: http://dx.doi.org/10.13039/100006238Award Number: Funding provided by: California Sea Grant, University of California, San DiegoCrossref Funder Registry ID: http://dx.doi.org/10.13039/100005522Award Number: Graduate Research Fellowship R/AQ-153FFunding provided by: National Geographic SocietyCrossref Funder Registry ID: http://dx.doi.org/10.13039/100006363Award Number: EC-53369R-18Funding provided by: National Oceanic and Atmospheric AdministrationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000192Award Number: Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: Graduate Research Fellowship DGE 1752814Funding provided by: Sonoma Fish and Wildlife Commission*Crossref Funder Registry ID: Award Number: Funding provided by: U.S. Army Corps of EngineersCrossref Funder Registry ID: http://dx.doi.org/10.13039/100006752Award Number:

The tool presented here is a BETA version, bringing together most of the tool functionalities discussed during the partner workshops. The aim of this version is to provide an overview of developments since the start of the Cimopolée project. It also enables users to report anomalies (comments on improvements, bugs, desired modifications, etc.) via an anomaly report..

GERALDINE is a free-to-use resource that enables the detection and characterisation of mass movements onto glaciers. Tool available at: GERALDINE (v1.1) Citation: Smith, W. D., Dunning, S. A., Brough, S., Ross, N., and Telling, J.: GERALDINE (Google Earth Engine supRaglAciaL Debris INput dEtector): a new tool for identifying and monitoring supraglacial landslide inputs, Earth Surf. Dynam., 8, 1053–1065, https://doi.org/10.5194/esurf-8-1053-2020, 2020. Version 1.1 removes the NDWI mask from the GERALDINE processing flow following reviewer comments.

This archive contains all relevant information to reproduce the experimental figures presented in the paper "A 5/4(1+eps)-Approximation Algorithm for Scheduling with Rejection Costs Proportional to Processing Times", as well as the scripts to re-run those experiments and new ones and process the results. All results presented in the paper are archived here as well.

The macroscale at which we measure, model, and predict climate change does not align with the microscale at which small ectotherms experience climate. To understand climate's influence on biodiversity and potential ecological effects of climate change, more work is needed to understand how ectotherm physiology relates to microclimatic temperatures. Tree canopies are an example of a habitat that produces extreme microclimates, and arthropods in tropical forest canopies are threatened by extreme heat and warming. The situation in temperate canopies, however, is less clear. Conventional wisdom suggests that winter cold limits arboreal arthropod diversity in temperate forests, but because the canopy is less buffered from extreme temperatures, summer heat could also play a role. Heat- and cold-limited communities will respond differently to climate change, so this distinction is critical. Using the frameworks of the thermal adaptation hypothesis and thermal niche asymmetry, we asked whether arboreal ants were physiologically adapted to their extreme environment and whether summer heat or winter cold was more stressful. We tracked internal microclimates of ant nests in the canopy and on the ground over the seasonal cycle in temperate forests in North Carolina, USA. Then, we measured the heat (CTmax) and cold tolerance (CTmin) of worker ants in summer and spring and compared them to the ants' experienced microclimates. Nests in the temperate canopy experienced hotter and colder extremes and more closely tracked air temperatures than ant nests on the ground. Arboreal ants partially adhered to the thermal adaptation hypothesis. They were more heat-tolerant than ground-nesting species, but despite experiencing lower temperatures, they were less cold-tolerant. Ants acclimated their cold tolerance in line with seasonal changes, but heat tolerance was more phylogenetically constrained. Summer heat did not approach ants' heat tolerance in either stratum, but winter and spring lows in the canopy exceeded the cold tolerance of ants nesting there. By comparing microclimatic temperatures and thermal physiology, we show that winter cold—and not summer heat—likely limits arthropod diversity in the temperate canopy. As the climate warms, the temperate canopy may become accessible to more arthropod species. Funding provided by: North Carolina State UniversityROR ID: https://ror.org/04tj63d06Award Number:

This repository contains the replication package for the paper "The EarlyBIRD Catches the Bug: On Exploiting Early Layers of Encoder Models for More Efficient Code Classification" by Anastasiia Grishina, Max Hort and Leon Moonen, accepted for publication in the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2023). The paper is deposited on arXiv and will be available under open access at the publisher's site (IEEE). The replication package is archived on Zenodo with DOI: 10.5281/zenodo.7608802. The source code is distributed under the MIT license, the data is distributed under the CC BY 4.0 license. Citation If you build on this data or code, please cite this work by referring to the paper: @inproceedings{grishina2023:earlybird, title = {The EarlyBIRD Catches the Bug: On Exploiting Early Layers of Encoder Models for More Efficient Code Classification}, author = {Anastasiia Grishina and Max Hort and Leon Moonen}, booktitle = {ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)}, year = {2023}, publisher = {ACM}, doi = {https://doi.org/10.1145/3611643.3616304}, note = {Pre-print on arXiv at https://arxiv.org/abs/2305.04940} } Organization The replication package is organized as follows: src - the source code requirements - txt files with Python packages and versions for replication data - all raw datasets used for training raw devign - Devign reveal - ReVeal break_it_fix_it - BIFI dataset exception - Exception Type dataset mlruns - results of experiments, the folder is created once the run.py is executed (see part II), empty folder at the time of distribution output - results of experiments tables mlflow_<dataset_name>.csv - we used MLflow to log metrics and parameters in our experiments and generated .csv files with the mlflow experiments csv -x <experiment_number> -o mlflow_<dataset_name>.csv command figures - figures reported in paper runs - folder to store model checkpoints, if the corresponding argument is provided when running the code model-checkpoints - models with the best F1-weighted score on each of the four datasets - one model for one dataset. Note that the best model is not always the model with the best average improvement over the baseline reported in the paper, because of possible best-performing outliers. This folder is distributed as a separate file called EarlyBIRD_model-checkpoints.zip (~4.5GB). notebooks - one Jupyter notebook with code to generate figures and tables with aggregated results as reported in the paper Usage Python version: 3.7.9 (later versions should also work well); CUDA version: 11.6; Git LFS. Commands below work well on Mac or Linux and should be adapted if you have a Windows machine. I. Set up data, environment and code 1. Path to project directory Update path/to/project to point at EarlyBIRD export EarlyBIRD=~/path/to/EarlyBIRD 2. Download codebert checkpoint Please, install Git LFS: https://docs.github.com/en/repositories/working-with-files/managing-large-files/installing-git-large-file-storage Run the following from within $EarlyBIRD/: cd $EarlyBIRD mkdir -p checkpoints/reused/model cd checkpoints/reused/model git lfs install git clone https://huggingface.co/microsoft/codebert-base cd codebert-base/ git lfs pull cd ../../.. 3. Set up a virtual environment cd $EarlyBIRD python -m venv venv source venv/bin/activate 3.1 No CUDA python -m pip install -r requirements/requirements_no_cuda.txt 3.2 With CUDA (to run on GPU) python -m pip install -r requirements/requirements_with_cuda.txt python -m pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116 4 Preprocess data After preprocessing, all datasets are stored in jsonlines (if in python) format. Naming convention: split is one of 'train', 'valid', 'test' in data/preprocessed-final/<dataset_name>/<split>.jsonl, with {'src': "def function_1() ...", 'label': "Label1"} {'src': "def function_2() ...", 'label': "Label2"} ... 4.1 Devign Raw data is downloaded from https://drive.google.com/file/d/1x6hoF7G-tSYxg8AFybggypLZgMGDNHfF/view. Test, train, valid txt files are downloaded from the https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection/ dataset. All files are saved in data/raw/devign. To preprocess raw data: cd $EarlyBIRD python -m src.preprocess \ --dataset_name devign \ --shrink_code \ --config_path src/config.yaml 4.2 ReVeal Raw data is downloaded from https://github.com/VulDetProject/ReVeal under "Our Collected vulnerabilities from Chrome and Debian issue trackers (Often referred as Chrome+Debian or Verum dataset in this project)" and saved in data/raw/reveal. To preprocess raw data: cd $EarlyBIRD python -m src.preprocess \ --dataset_name reveal \ --shrink_code \ --config_path src/config.yaml 4.3 Break-it-fix-it Raw data is downloaded as data_minimal.zip from https://github.com/michiyasunaga/BIFI under p. 1, unzipped, and the folder orig_bad_code is saved in data/raw/break_it_fix_it. To preprocess raw data: cd $EarlyBIRD python -m src.preprocess \ --dataset_name break_it_fix_it \ --shrink_code \ --ratio_train 0.9 \ --config_path src/config.yaml Note: The original paper contains only train and test split. Use --ratio_train to specify what part of the original train (orig-train) split will be used in train and the rest of orig-train will be used for validation during training. 4.4 Exception Type Raw data is downloaded from https://github.com/google-research/google-research/tree/master/cubert under "2. Exception classification" (it points to this storage) and saved in data/raw/exception_type. To preprocess raw data: cd $EarlyBIRD python -m src.preprocess \ --dataset_name exception \ --shrink_code \ --config_path src/config.yaml II. Run code Activate virtual environment (if not done so yet): cd $EarlyBIRD source venv/bin/activate Example run Run experiments with Devign using pruned models (cutoff_layers_one_layer_cls) to 3 layers (--hidden_layer_to_use 3), for example: cd $EarlyBIRD python -m src.run --help # for help with command line args python -m src.run \ --config_path src/config.yaml \ --model_name codebert \ --model_path "checkpoints/reused/model/codebert-base" \ --tokenizer_path "checkpoints/reused/model/codebert-base" \ --dataset_name devign \ --benchmark_name acc \ --train \ --test \ -warmup 0 \ --device cuda \ --epochs 10 \ -clf one_linear_layer \ --combination_type cutoff_layers_one_layer_cls \ --hidden_layer_to_use 3 \ --experiment_no 12 \ --seed 42 To run experiments on a small subset of data, use --debug argument. For example: python -m src.run \ --debug \ --config_path src/config.yaml \ --model_name codebert \ --model_path "checkpoints/reused/model/codebert-base" \ --tokenizer_path "checkpoints/reused/model/codebert-base" \ --dataset_name devign \ --benchmark_name acc \ --train \ --test \ -warmup 0 \ --device cuda \ --epochs 2 \ -clf one_linear_layer \ --combination_type cutoff_layers_one_layer_cls \ --hidden_layer_to_use 3 \ --experiment_no 12 \ --seed 42 Explore output Your EarlyBIRD/ should contain mlruns/. If you started the run.py from another location, you will find mlruns/one level below that location. cd $EarlyBIRD mlflow ui Alternatively, find tables in EarlyBIRD/output/tables/ with best epoch logs and logs of all epochs. ChangeLog v1.0 - corresponds to the version submitted for review to ESEC/FSE 2023 and contains code for using CodeBERT as a base model for fine-tuning, extensive logging in MLFlow and a custom table, as well as replication instructions. v1.1 - corresponds to the camera-ready submission for ESEC/FSE 2023 and contains the code with configurations adapted to use more models for fine-tuning, logging in MLFlow (redundant logging in a custom table is removed), Jupyter notebooks to replicate artifacts in the paper, as well as replication instructions and model checkpoints. Acknowledgement The work included in this repository was supported by the Research Council of Norway through the secureIT project (IKTPLUSS #288787). Max Hort is supported through the ERCIM 'Alain Bensoussan' Fellowship Programme. The empirical evaluation was performed on the Experimental Infrastructure for Exploration of Exascale Computing (eX3), financially supported by the Research Council of Norway under contract #270053, as well as on resources provided by Sigma2, the National Infrastructure for High Performance Computing and Data Storage in Norway.

ERIGrid JRA2: Test case TC3 mosaik implementation